






'''simple docstring'''





import argparse
import os
import re

import packaging.version


_UpperCamelCase						:						List[str]  =					'examples/'
_UpperCamelCase						:						int  =					{
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
_UpperCamelCase						:						Any  =					{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
_UpperCamelCase						:						Union[str, Any]  =					'README.md'
def __UpperCAmelCase   (					A					:			Dict			,       A					:			Any			,       A					:			List[Any]		)    ->   Union[str, Any]:
			with open(A			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						UpperCAmelCase_      :       Optional[int]          =       f.read()
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       REPLACE_PATTERNS[pattern]
			UpperCAmelCase_      :       List[str]          =       replace.replace('''VERSION'''			,       A		)
			UpperCAmelCase_      :       List[str]          =       re_pattern.sub(A			,       A		)
			with open(A			,       '''w'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						f.write(A		)
def __UpperCAmelCase   (					A					:			List[str]		)    ->   int:
			for folder, directories, fnames in os.walk(A		):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove('''research_projects'''		)
						if "legacy" in directories:
									directories.remove('''legacy'''		)
						for fname in fnames:
									if fname.endswith('''.py'''		):
												update_version_in_file(os.path.join(A			,       A		)			,       A			,       pattern='''examples'''		)
def __UpperCAmelCase   (					A					:			Dict			,       A					:			str=False		)    ->   int:
			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(A			,       A			,       A		)
			if not patch:
						update_version_in_examples(A		)
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       Any          =       '''🤗 Transformers currently provides the following architectures'''
			UpperCAmelCase_      :       Optional[int]          =       '''1. Want to contribute a new model?'''
			with open(A			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						UpperCAmelCase_      :       Dict          =       f.readlines()

			# Find the start of the list.
			UpperCAmelCase_      :       List[Any]          =       0
			while not lines[start_index].startswith(_start_prompt		):
						start_index += 1
			start_index += 1

			UpperCAmelCase_      :       List[str]          =       start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt		):
						if lines[index].startswith('''1.'''		):
									UpperCAmelCase_      :       int          =       lines[index].replace(
									    '''https://huggingface.co/docs/transformers/main/model_doc'''			,       '''https://huggingface.co/docs/transformers/model_doc'''			,       )
						index += 1

			with open(A			,       '''w'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						f.writelines(A		)
def __UpperCAmelCase   (					)    ->   Dict:
			with open(REPLACE_FILES['''init''']			,       '''r'''		) as f:
						UpperCAmelCase_      :       Any          =       f.read()
			UpperCAmelCase_      :       List[str]          =       REPLACE_PATTERNS['''init'''][0].search(A		).groups()[0]
			return packaging.version.parse(A		)
def __UpperCAmelCase   (					A					:			List[str]=False		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[Any]          =       get_version()
			if patch and default_version.is_devrelease:
						raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''		)
			if default_version.is_devrelease:
						UpperCAmelCase_      :       Any          =       default_version.base_version
			elif patch:
						UpperCAmelCase_      :       Tuple          =       F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
			else:
						UpperCAmelCase_      :       Tuple          =       F"{default_version.major}.{default_version.minor + 1}.0"

			# Now let's ask nicely if that's the right one.
			UpperCAmelCase_      :       List[Any]          =       input(F"Which version are you releasing? [{default_version}]"		)
			if len(A		) == 0:
						UpperCAmelCase_      :       Dict          =       default_version

			print(F"Updating version to {version}."		)
			global_version_update(A			,       patch=A		)
			if not patch:
						print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''		)
						clean_main_ref_in_model_list()
def __UpperCAmelCase   (					)    ->   Dict:
			UpperCAmelCase_      :       List[Any]          =       get_version()
			UpperCAmelCase_      :       List[Any]          =       F"{current_version.major}.{current_version.minor + 1}.0.dev0"
			UpperCAmelCase_      :       int          =       current_version.base_version

			# Check with the user we got that right.
			UpperCAmelCase_      :       Tuple          =       input(F"Which version are we developing now? [{dev_version}]"		)
			if len(A		) == 0:
						UpperCAmelCase_      :       Union[str, Any]          =       dev_version

			print(F"Updating version to {version}."		)
			global_version_update(A		)
			print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''		)
			clean_main_ref_in_model_list()


if __name__ == "__main__":
						_UpperCamelCase						:						List[str]  =					argparse.ArgumentParser()
						parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
						parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
						_UpperCamelCase						:						Dict  =					parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print('Nothing to do after a patch :-)')
						else:
												post_release_work()










'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					StableDiffusionInstructPixaPixPipeline
					a_        =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					a_        =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a_        =					IMAGE_TO_IMAGE_IMAGE_PARAMS
					a_        =					IMAGE_TO_IMAGE_IMAGE_PARAMS




					def 		A      (  self  :		str      )    ->							Optional[Any]:
								torch.manual_seed(0      )
								UpperCAmelCase_      :       Tuple          =       UNetaDConditionModel(
								    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=8				, out_channels=4				, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				, cross_attention_dim=32				, )
								UpperCAmelCase_      :       Any          =       PNDMScheduler(skip_prk_steps=_A      )
								torch.manual_seed(0      )
								UpperCAmelCase_      :       int          =       AutoencoderKL(
								    block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				, latent_channels=4				, )
								torch.manual_seed(0      )
								UpperCAmelCase_      :       Tuple          =       CLIPTextConfig(
								    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1e-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=10_00				, )
								UpperCAmelCase_      :       List[str]          =       CLIPTextModel(_A      )
								UpperCAmelCase_      :       List[str]          =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

								UpperCAmelCase_      :       Union[str, Any]          =       {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components




					def 		A      (  self  :		List[str]				, _A  :		Tuple				, _A  :		str=0      )    ->							int:
								UpperCAmelCase_      :       Tuple          =       floats_tensor((1, 3, 32, 32)				, rng=random.Random(_A      )      ).to(_A      )
								UpperCAmelCase_      :       Dict          =       image.cpu().permute(0				, 2				, 3				, 1      )[0]
								UpperCAmelCase_      :       Optional[Any]          =       Image.fromarray(np.uinta(_A      )      ).convert('''RGB'''      )
								if str(_A      ).startswith('''mps'''      ):
											UpperCAmelCase_      :       str          =       torch.manual_seed(_A      )
								else:
											UpperCAmelCase_      :       Optional[Any]          =       torch.Generator(device=_A      ).manual_seed(_A      )
								UpperCAmelCase_      :       List[str]          =       {
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''image_guidance_scale''': 1,
								    '''output_type''': '''numpy''',
								}
								return inputs




					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       Any          =       self.get_dummy_components()
								UpperCAmelCase_      :       int          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Tuple          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       str          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       int          =       image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Union[str, Any]          =       np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		int      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       List[str]          =       self.get_dummy_components()
								UpperCAmelCase_      :       Dict          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       str          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       List[Any]          =       '''french fries'''
								UpperCAmelCase_      :       Optional[Any]          =       sd_pipe(**_A				, negative_prompt=_A      )
								UpperCAmelCase_      :       Tuple          =       output.images
								UpperCAmelCase_      :       Union[str, Any]          =       image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Dict          =       np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		Dict      )    ->							str:
								UpperCAmelCase_      :       List[str]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       List[Any]          =       self.get_dummy_components()
								UpperCAmelCase_      :       Any          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       int          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       Any          =       [inputs['''prompt''']] * 2

								UpperCAmelCase_      :       int          =       np.array(inputs['''image''']      ).astype(np.floataa      ) / 255.0
								UpperCAmelCase_      :       int          =       torch.from_numpy(_A      ).unsqueeze(0      ).to(_A      )
								UpperCAmelCase_      :       List[str]          =       image / 2 + 0.5
								UpperCAmelCase_      :       str          =       image.permute(0				, 3				, 1				, 2      )
								UpperCAmelCase_      :       Any          =       image.repeat(2				, 1				, 1				, 1      )

								UpperCAmelCase_      :       List[Any]          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       str          =       image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								UpperCAmelCase_      :       Optional[Any]          =       np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		List[str]      )    ->							List[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       Tuple          =       self.get_dummy_components()
								UpperCAmelCase_      :       List[str]          =       EulerAncestralDiscreteScheduler(
								    beta_start=0.00_085				, beta_end=0.012				, beta_schedule='''scaled_linear'''      )
								UpperCAmelCase_      :       List[Any]          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Dict          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Tuple          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       str          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       Dict          =       image[0, -3:, -3:, -1]

								UpperCAmelCase_      :       Dict          =       [round(_A				, 4      ) for x in image_slice.flatten().tolist()]
								print(''','''.join([str(_A      ) for x in slice]      )      )

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Any          =       np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		str      )    ->							List[str]:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





					def 		A      (  self  :		List[Any]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Tuple          =       self.get_dummy_components()
								UpperCAmelCase_      :       Tuple          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Any          =       VaeImageProcessor(do_resize=_A				, do_normalize=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       int          =       pipe(**self.get_dummy_inputs_by_type(_A				, input_image_type='''pt'''      )      )[0]

								UpperCAmelCase_      :       Union[str, Any]          =       components['''vae''']
								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs_by_type(_A				, input_image_type='''pt'''      )

								for image_param in self.image_latents_params:
											if image_param in inputs.keys():
														UpperCAmelCase_      :       int          =       vae.encode(inputs[image_param]      ).latent_dist.mode()

								UpperCAmelCase_      :       List[Any]          =       pipe(**_A      )[0]

								UpperCAmelCase_      :       int          =       np.abs(out - out_latents_inputs      ).max()
								self.assertLess(_A				, 1e-4				, '''passing latents as image input generate different result from passing image'''      )






@slow
@require_torch_gpu
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Any      )    ->							Optional[Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




					def 		A      (  self  :		List[str]				, _A  :		Optional[Any]=0      )    ->							List[Any]:
								UpperCAmelCase_      :       Dict          =       torch.manual_seed(_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''      )
								UpperCAmelCase_      :       Optional[int]          =       {
								    '''prompt''': '''turn him into a cyborg''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''image_guidance_scale''': 1.0,
								    '''output_type''': '''numpy''',
								}
								return inputs




					def 		A      (  self  :		Optional[int]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       List[Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       List[str]          =       self.get_inputs()
								UpperCAmelCase_      :       int          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Dict          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       Any          =       np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		Optional[Any]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								UpperCAmelCase_      :       Dict          =       LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       List[Any]          =       self.get_inputs()
								UpperCAmelCase_      :       str          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Optional[int]          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       List[str]          =       np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		List[str]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       str          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								UpperCAmelCase_      :       str          =       DDIMScheduler.from_config(pipe.scheduler.config      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Any          =       self.get_inputs()
								UpperCAmelCase_      :       Any          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Optional[Any]          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       Dict          =       np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       0

								def callback_fn(_A  :		int				, _A  :		int				, _A  :		torch.FloatTensor      ) -> None:
											UpperCAmelCase_      :       Tuple          =       True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
														UpperCAmelCase_      :       List[str]          =       latents.detach().cpu().numpy()
														assert latents.shape == (1, 4, 64, 64)
														UpperCAmelCase_      :       Optional[Any]          =       latents[0, -3:, -3:, -1]
														UpperCAmelCase_      :       List[Any]          =       np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]      )

														assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
											elif step == 2:
														UpperCAmelCase_      :       Union[str, Any]          =       latents.detach().cpu().numpy()
														assert latents.shape == (1, 4, 64, 64)
														UpperCAmelCase_      :       Dict          =       latents[0, -3:, -3:, -1]
														UpperCAmelCase_      :       Tuple          =       np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]      )

														assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

								UpperCAmelCase_      :       Dict          =       False

								UpperCAmelCase_      :       Dict          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A				, torch_dtype=torch.floataa      )
								UpperCAmelCase_      :       Any          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Dict          =       self.get_inputs()
								pipe(**_A				, callback=_A				, callback_steps=1      )
								assert callback_fn.has_been_called
								assert number_of_steps == 3




					def 		A      (  self  :		int      )    ->							int:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCAmelCase_      :       Union[str, Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A				, torch_dtype=torch.floataa      )
								UpperCAmelCase_      :       Tuple          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing(1      )
								pipe.enable_sequential_cpu_offload()

								UpperCAmelCase_      :       Dict          =       self.get_inputs()
								UpperCAmelCase_      :       List[str]          =       pipe(**_A      )

								UpperCAmelCase_      :       Dict          =       torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 10**9





					def 		A      (  self  :		str      )    ->							int:
								UpperCAmelCase_      :       List[Any]          =       self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								UpperCAmelCase_      :       List[str]          =       inputs['''image'''].resize((5_04, 5_04)      )

								UpperCAmelCase_      :       int          =       '''timbrooks/instruct-pix2pix'''
								UpperCAmelCase_      :       Tuple          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    _A				, safety_checker=_A				, )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Optional[Any]          =       pipe(**_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       output.images[0]

								UpperCAmelCase_      :       List[str]          =       image[2_55:2_58, 3_83:3_86, -1]

								assert image.shape == (5_04, 5_04, 3)
								UpperCAmelCase_      :       List[str]          =       np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3


